Deep learning of unresolved turbulent ocean processes in climate models

Laure Zanna, Thomas Bolton

Research output: Chapter in Book/Report/Conference proceedingChapter


Climate models are an approximate representation of the laws of physics describing the evolution of the ocean and atmosphere dynamics. Due to limited computational resources, many ocean processes, which are crucial for the transport of heat and carbon, occur at scales smaller than the grid resolution of climate models. Therefore, we rely on approximations, called parameterizations, to represent these unresolved processes in climate models. Parameterizations, traditionally derived from semi-empirical or idealized theories, are often imperfect and can lead to biases in climate models. Machine learning algorithms, and deep learning (DL) algorithms in particular, could provide an avenue to improve the representation of unresolved processes in ocean models by efficiently extracting information from high-resolution ocean simulations and/or observational data, potentially enhancing the skill of parameterizations in climate models.

Original languageEnglish (US)
Title of host publicationDeep Learning for the Earth Sciences
Subtitle of host publicationA Comprehensive Approach to Remote Sensing, Climate Science and Geosciences
Number of pages9
ISBN (Electronic)9781119646181
ISBN (Print)9781119646143
StatePublished - Aug 20 2021


  • Climate models
  • Data-driven parameterizations
  • Deep learning
  • Eddy field
  • Machine learning
  • Physics-driven parameterizations
  • Unresolved ocean processes

ASJC Scopus subject areas

  • General Engineering
  • General Earth and Planetary Sciences


Dive into the research topics of 'Deep learning of unresolved turbulent ocean processes in climate models'. Together they form a unique fingerprint.

Cite this